Browsing by Author "Tagliasacchi, Andrea"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    LSMAT Least Squares Medial Axis Transform
    (© 2019 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2019) Rebain, Daniel; Angles, Baptiste; Valentin, Julien; Vining, Nicholas; Peethambaran, Jiju; Izadi, Shahram; Tagliasacchi, Andrea; Chen, Min and Benes, Bedrich
    The medial axis transform has applications in numerous fields including visualization, computer graphics, and computer vision. Unfortunately, traditional medial axis transformations are usually brittle in the presence of outliers, perturbations and/or noise along the boundary of objects. To overcome this limitation, we introduce a new formulation of the medial axis transform which is naturally robust in the presence of these artefacts. Unlike previous work which has approached the medial axis from a computational geometry angle, we consider it from a numerical optimization perspective. In this work, we follow the definition of the medial axis transform as ‘the set of maximally inscribed spheres’. We show how this definition can be formulated as a least squares relaxation where the transform is obtained by minimizing a continuous optimization problem. The proposed approach is inherently parallelizable by performing independent optimization of each sphere using Gauss–Newton, and its least‐squares form allows it to be significantly more robust compared to traditional computational geometry approaches. Extensive experiments on 2D and 3D objects demonstrate that our method provides superior results to the state of the art on both synthetic and real‐data.The medial axis transform has applications in numerous fields including visualization, computer graphics, and computer vision. Unfortunately, traditional medial axis transformations are usually brittle in the presence of outliers, perturbations and/or noise along the boundary of objects. To overcome this limitation, we introduce a new formulation of the medial axis transform which is naturally robust in the presence of these artefacts. Unlike previous work which has approached the medial axis from a computational geometry angle, we consider it from a numerical optimization perspective. In this work, we follow the definition of the medial axis transform as ‘the set of maximally inscribed spheres’.
  • No Thumbnail Available
    Item
    A Survey of Surface Reconstruction from Point Clouds
    (© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017) Berger, Matthew; Tagliasacchi, Andrea; Seversky, Lee M.; Alliez, Pierre; Guennebaud, Gaël; Levine, Joshua A.; Sharf, Andrei; Silva, Claudio T.; Chen, Min and Zhang, Hao (Richard)
    The area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contain a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece‐wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the reconstruction can take on different representations—not necessarily the explicit geometry. We survey the field of surface reconstruction, and provide a categorization with respect to priors, data imperfections and reconstruction output. By considering a holistic view of surface reconstruction, we show a detailed characterization of the field, highlight similarities between diverse reconstruction techniques and provide directions for future work in surface reconstruction.The area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contain a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece‐wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the reconstruction can take on different representations—not necessarily the explicit geometry
  • Thumbnail Image
    Item
    Volumetric Video - Acquisition, Compression, Interaction and Perception
    (The Eurographics Association, 2021) Zell, Eduard; Castan, Fabien; Gasparini, Simone; Hilsmann, Anna; Kazhdan, Misha; Tagliasacchi, Andrea; Zarpalas, Dimitris; Zioulis, Nick; O'Sullivan, Carol and Schmalstieg, Dieter
    Volumetric video, free-viewpoint video or 4D reconstruction refer to the process of reconstructing 3D content over time using a multi-view setup. This method is constantly gaining popularity both in research and industry. In fact, volumetric video is more and more considered to acquire dynamic photorealistic content instead of relying on traditional 3D content creation pipelines. The aim of the tutorial is to provide an overview of the entire volumetric video pipeline. Furthermore, it presents existing projects that may serve as a starting point to this topic at the intersection of computer vision and graphics. The first part of the tutorial will focus on the process of computing 3D models from captured videos. Topics will include content acquisition with affordable hardware, photogrammetry, and surface reconstruction from point clouds. A remarkable contribution of the presenters to the graphics community is that they will not only provide an overview of their topic but have in addition open sourced their implementations. Topics of the second part will focus on usage and distribution of volumetric video, including data compression, streaming or post-processing like pose-modification or seamless blending. The tutorial will conclude with an overview of perceptual studies focusing on quality assessment of 3D and 4D content.